A.: Effective use of wordnet semantics via kernel-based learning

The work on document similarity has shown that complex representations are not more accurate than the simple bag-of-words. Term clustering, e.g. using latent semantic indexing, word co-occurrences or synonym relations using a word ontol-ogy have been shown not very effective. In particular, when to extend the similar-ity function external prior knowledge is used, e.g. WordNet, the retrieval system decreases its performance. The critical is-sues here are methods and conditions to integrate such knowledge. In this paper we propose kernel func-tions to add prior knowledge to learn-ing algorithms for document classifica-tion. Such kernels use a term similarity measure based on the WordNet hierarchy. The kernel trick is used to implement such space in a balanced and statistically co-herent way. Cross-validation results show the benefit of the approach for the Support Vector Machines when few training data is available.